Instructions to use SJTU-DENG-Lab/MBD-Math-SDAR-8B-Chat-b4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SJTU-DENG-Lab/MBD-Math-SDAR-8B-Chat-b4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SJTU-DENG-Lab/MBD-Math-SDAR-8B-Chat-b4", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("SJTU-DENG-Lab/MBD-Math-SDAR-8B-Chat-b4", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SJTU-DENG-Lab/MBD-Math-SDAR-8B-Chat-b4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SJTU-DENG-Lab/MBD-Math-SDAR-8B-Chat-b4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SJTU-DENG-Lab/MBD-Math-SDAR-8B-Chat-b4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SJTU-DENG-Lab/MBD-Math-SDAR-8B-Chat-b4

SGLang

How to use SJTU-DENG-Lab/MBD-Math-SDAR-8B-Chat-b4 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SJTU-DENG-Lab/MBD-Math-SDAR-8B-Chat-b4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SJTU-DENG-Lab/MBD-Math-SDAR-8B-Chat-b4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SJTU-DENG-Lab/MBD-Math-SDAR-8B-Chat-b4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SJTU-DENG-Lab/MBD-Math-SDAR-8B-Chat-b4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SJTU-DENG-Lab/MBD-Math-SDAR-8B-Chat-b4 with Docker Model Runner:
```
docker model run hf.co/SJTU-DENG-Lab/MBD-Math-SDAR-8B-Chat-b4
```

MBD-Math-SDAR-8B-Chat-b4 / README.md

nielsr HF Staff

Add model card, metadata, and links to paper and code

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	---
	license: mit
	library_name: transformers
	pipeline_tag: text-generation
	---

	# Multi-Block Diffusion Language Models (MBD-LMs)

	This repository contains the model weights for Multi-Block Diffusion Language Models (MBD-LMs), presented in the paper [Multi-Block Diffusion Language Models](https://huggingface.co/papers/2606.29215).

	- Project Page: [sjtu-deng-lab.github.io/mbd-lms](https://sjtu-deng-lab.github.io/mbd-lms/)
	- GitHub Repository: [SJTU-DENG-Lab/mbd-lms](https://github.com/SJTU-DENG-Lab/mbd-lms)
	- Paper: [arXiv:2606.29215](https://huggingface.co/papers/2606.29215)

	## Introduction

	Block Diffusion Language Models (BD-LMs) improve diffusion-based text generation with KV caching and flexible-length generation. MBD-LMs extend them from Single-Block Diffusion (SingleBD) to Multi-Block Diffusion (MultiBD), allowing a running-set of consecutive blocks to be decoded concurrently for inter-block parallelism.

	To train these models, the authors propose Multi-block Teacher Forcing (MultiTF), which post-trains BD-LMs with randomized noise-schedulers that better match MultiBD inference states.

	## Citation

	If you find this work useful, please consider citing the paper:

	```bibtex
	@article{jin2026multiblock,
	title={Multi-Block Diffusion Language Models},
	author={Jin, Yijie and Xu, Jiajun and Liu, Yuxuan and Xu, Chenkai and Tu, Yi and Li, Jiajun and Tu, Dandan and Yan, Xiaohui and Yu, Kai and Liu, Pengfei and Deng, Zhijie},
	journal={arXiv preprint arXiv:2606.29215},
	year={2026}
	}
	```